Community detection in national-scale high voltage transmission networks using genetic algorithms |
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| Affiliation: | 1. CeiA3, Department of Informatics, University of Almería, Carretera de Sacramento s/n, 04120 Almería Spain;2. CeiA3, Department of Engineering, University of Almería, Carretera de Sacramento s/n, 04120 Almería Spain;1. CEBE, Birmingham City University, Birmingham, UK;2. COCIS, Edinburgh Napier University, Edinburgh, UK;1. Arup, 13 Fitzroy St, London W1T 4BQ, UK;2. Laing O’Rourke Reader, Dept. of Engineering, Univ. of Cambridge, Trumpington St., Cambridge CB2 1PZ, UK;1. Sonny Astani Dept. of Civil and Environmental Engineering, Univ. of Southern California, KAP 217, 3620 South Vermont Ave., Los Angeles, CA 90089-2531, United States;2. Sonny Astani Dept. of Civil and Environmental Engineering, Univ. of Southern California, KAP 224C, 3620 South Vermont Ave., Los Angeles, CA 90089-2531, United States;3. Civil and Environmental Engineering Department, Carnegie Mellon University, Porter Hall 113, Pittsburgh, PA 15213-3890, United States;4. Sonny Astani Dept. of Civil and Environmental Engineering, Univ. of Southern California, KAP 210A, 3620 South Vermont Ave., Los Angeles, CA 90089-2531, United States;1. Data Science Institute, Imperial College London, United Kingdom;2. Foresight and Innovation, Arup, United Kingdom;1. Department of Engineering, CEIA3, University of Almeria, Almeria, Spain;2. Department of Computer Science and Engineering Electronic, Universidad de la Costa, Barranquilla, Colombia |
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| Abstract: | The large-scale interconnection of electricity networks has been one of the most important investments made by electric companies, and this trend is expected to continue in the future. One of the research topics in this field is the application of graph-based analysis to identify the characteristics of power grids. In particular, the application of community detection techniques allows for the identification of network elements that share valuable properties by partitioning a network into some loosely coupled sub-networks (communities) of similar scale, such that nodes within a community are densely linked, while connections between different communities are sparser. This paper proposes the use of competitive genetic algorithms to rapidly detect any number of community structures in complex grid networks. Results obtained in several national- scale high voltage transmission networks, including Italy, Germany, France, the Iberian peninsula (Spain and Portugal), Texas (US), and the IEEE 118 bus test case that represents a portion of the American Electric Power System (in the Midwestern US), show the good performance of genetic algorithms to detect communities in power grids. In addition to the topological analysis of power grids, the implications of these results from an engineering point of view are discussed, as well as how they could be used to analyze the vulnerability risk of power grids to avoid large-scale cascade failures. |
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| Keywords: | Electric power system Power grid High voltage transmission networks Contingency analysis Community detection Genetic algorithms |
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